ArticleStatistics in medicine2008
Modeling heaping in self-reported cigarette counts.
Article in Statistics in medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Statistical analysis of daily smoking status in smoking cessation clinical trials.Addiction (Abingdon, England) · 2011Trial
- Epidemic thresholds across sexual behavior groups in the UK Biobank.Frontiers in reproductive health · 2026Article
- A Primer on Large-Sample Statistical Inference for Epidemiologists.Epidemiologic methods · 2026Article
- Explaining "U-shaped" distributions of population substance use with a simple computational approach.Journal of artificial societies and social simulation : JASSS · 2025Article
- EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN.The annals of applied statistics · 2024Article
- Models for Zero-Inflated and Overdispersed Correlated Count Data: An Application to Cigarette Use.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- Smoking Disparities by Level of Educational Attainment and Birth Cohort in the U.S.American journal of preventive medicine · 2023Article
- A method of correction for heaping error in the variables using validation data.Statistical papers (Berlin, Germany) · 2023Article
- Article
- Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2021Article
- To bin or not to bin? A comparison of symptom frequency response formats in the assessment of health-related quality of life.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2019Article
- Models for analyzing zero-inflated and overdispersed count data: an application to cigarette and marijuana use.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2018Article
- Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.Statistical methods in medical research · 2018Article
- Study of depression influencing factors with zero-inflated regression models in a large-scale population survey.BMJ open · 2017Article
- Zero-inflated count models for longitudinal measurements with heterogeneous random effects.Statistical methods in medical research · 2017Article
- The Importance of Filter Collection for Accurate Measurement of Cigarette Smoking.Tobacco regulatory science · 2017Article
- Heaping at Round Numbers on Financial Questions: The Role of Satisficing.Survey research methods · 2017Article
- Model-based imputation of latent cigarette counts using data from a calibration study.International journal of methods in psychiatric research · 2016Article
- Rounding behavior in the reporting of headache frequency complicates headache chronification research.Headache · 2013Article
- EVALUATING COSTS WITH UNMEASURED CONFOUNDING: A SENSITIVITY ANALYSIS FOR THE TREATMENT EFFECT.The annals of applied statistics · 2013Article
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Abstract
In studies of smoking behavior, some subjects report exact cigarette counts, whereas others report rounded-off counts, particularly multiples of 20, 10 or 5. This form of data reporting error, known as heaping, can bias the estimation of parameters of interest such as mean cigarette consumption. We present a model to describe heaped count data from a randomized trial of bupropion treatment for smoking cessation. The model posits that the reported cigarette count is a deterministic function of an underlying precise cigarette count variable and a heaping behavior variable, both of which are at best partially observed. To account for an excess of zeros, as would likely occur in a smoking cessation study where some subjects successfully quit, we model the underlying count variable with zero-inflated count distributions. We study the sensitivity of the inference on smoking cessation by fitting various models that either do or do not account for heaping and zero inflation, comparing the models by means of Bayes factors. Our results suggest that sufficiently rich models for both the underlying distribution and the heaping behavior are indispensable to obtaining a good fit with heaped smoking data. The analyses moreover reveal that bupropion has a significant effect on the fraction abstinent, but not on mean cigarette consumption among the non-abstinent.
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